Standard Deep Learning-based detectors generally face a trade-off between accuracy and latency, as well as a significant performance degradation when applied to unseen environments. To address these challenges, this study proposes a method that enhances both accuracy and latency by leveraging the static characteristics of fixed-camera pig pen monitoring. Specifically, we utilize background and infrastructure information obtained through a one-time preprocessing step upon camera installation. By integrating this information, we introduce three distinct modules, Background-suppressed Image Generator (BIG), Facility Image Generator (FIG), and Background Suppression Integration (BSI), that improve detection accuracy and operational efficiency without the need for model retraining. BIG creates background-suppressed images that integrate foreground and background information. FIG creates facility mask images that can be used to identify pigs that are occluded by facilities, enabling more efficient learning in unseen environments. BSI leverages both the input image and the background-suppressed image generated by BIG, feeding them into a 3D convolution layer for efficient feature fusion. This difference-aware fusion helps the model focus on foreground information and gradually reduce the domain gap. After training on the German pig dataset and testing on the unseen Korean Hadong pig dataset, the proposed method could improve AP₅₀ accuracy (from 75% to 86%) and Jetson Orin Nano latency (from 67 ms to 41 ms) compared to the baseline model YOLOV12m.

Identifying poor-laying hens is important for reducing feed waste, but visual screening is labor-intensive. Motivated by the relationship between laying status and pigmentation, we propose CHIC (Color-Histogram Integrated Classifier), a lightweight model combining HVI (Horizontal, Vertical, Intensity) transformation with differentiable histogram encoding of learned feature activations and global-average-pooled features. Evaluation used a four-class head-and-leg patch dataset collected from 20 hens in six cages at a single facility, with cage-level laying-status labels. Across ten training runs with different development-data folds and a fixed frame-level test set, CHIC achieved mean binary F1-scores of 92.21% for head patches and 96.06% for leg patches, exceeding the MobileNetV4-S baselines. With 2.69 million parameters, the classifier had a mean forward-pass latency of 21.87 ms per patch on an NVIDIA Jetson Orin Nano Super. Component comparisons showed configuration-dependent effects; histogram integration also benefited grayscale inputs, while mean–standard-deviation pooling with HVI inputs achieved similar mean performance to CHIC. The classification advantage was maintained when predictions from detected head and leg regions were aggregated into hen-level decisions. However, performance declined substantially on unseen cages. These findings support CHIC as a candidate for automated poor-laying hen screening, with further validation required across independent hens and farms.